Rapid metabolite identification with sub parts‐per‐million mass accuracy from biological matrices by direct infusion nanoelectrospray ionization after clean‐up on a ZipTip and LTQ/Orbitrap mass spectrometry
Bibliographic record
Abstract
Metabolite identification studies remain an integral part of pre-clinical and clinical drug development programs. Analysis of biological matrices, such as plasma, urine, feces and bile, pose challenges due to the large amounts of endogenous components that can mask a drug and its metabolites. Although direct infusion nanoelectrospray using capillaries has been used routinely for proteomic studies, metabolite identification has traditionally employed liquid chromatographic (LC) separation prior to analysis. A method is described here for rapid metabolite profiling in biological fluids that involves initial sample clean-up using pipette tips packed with reversed-phase material (i.e. ZipTips) to remove matrix components followed by direct infusion nanoelectrospray on an LTQ/Orbitrap mass spectrometer using a protonated polydimethylcyclosiloxane cluster ion for internal calibration. We re-examined samples collected from a prazosin metabolism study in the rat. Results are presented that demonstrate that sub parts-per-million accuracies can be achieved on molecular ions, facilitating identification of metabolites, and on product ions, facilitating structural assignments. The data also show that the high-resolution measurements (R = 100,000 at m/z 400) enable metabolites of interest to be resolved from endogenous components. The extended analysis times available with nanospray enables signal averaging for 1 min or more that is valuable when metabolites are present in low concentrations as encountered here in plasma and brain. Using this approach, the metabolic fate of a drug can be quickly obtained. A limitation of this approach is that metabolites that are structural isomers cannot be distinguished, although such information can be collected by LC/MS during follow-on experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".